Underwater Visual Acoustic SLAM with Extrinsic Calibration
Shida Xu, Tomasz Luczynski, Jonatan Scharff Willners, Ziyang Hong, Kaicheng Zhang, Yvan R. Petillot, Sen Wang
Abstract
Underwater scenarios are challenging for visual Simultaneous Localization and Mapping (SLAM) due to limited visibility and intermittently losing structures in image views. In this paper, we propose a visual acoustic bundle adjustment system which fuses a camera and a Doppler Velocity Log (DVL) in a graph SLAM framework for reliable underwater localization and mapping. In order to fuse the vision with the acoustic measurements, an calibration algorithm is also designed to estimate extrinsic parameters between a camera and a DVL using features detected in scenes. Experimental results in a tank and an offshore wind farm show the proposed method can achieve better robustness and localization accuracy than pure visual SLAM, especially in visually challenging scenarios, and the extrinsic calibration parameters can be accurately estimated, even when initialized with a random guess.
BibTeX
@inproceedings{iros2021_underwatervisual,
title = {Underwater Visual Acoustic SLAM with Extrinsic Calibration},
author = {Shida Xu and Tomasz Luczynski and Jonatan Scharff Willners and Ziyang Hong and Kaicheng Zhang and Yvan R. Petillot and Sen Wang},
booktitle = {IROS 2021},
year = {2021}
}